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Assessment of the Levels of Level of Biomarkers of Bone Matrix Glycoproteins and Inflammatory Cytokines from Saudi Parkinson Patients.

Aziza Alrafiah | Ebtisam Al-Ofi | Mona Talib Obaid | Nimah Alsomali
BioMed research international | 2019

Background. Parkinson's disease (PD) is the second most commonly neurodegenerative disease after Alzheimer's disease which occurs to nearly 1% of the population > 50 years old. Inflammatory and bone biomarkers have both become valuable tools for PD diagnosis and prognosis. However, no studies have examined these markers in Saudi patients diagnosed with PD. Objectives. To assess the biomarkers and proinflammatory cytokines from blood with PD in serum. Methods. In our study, we included 26 patients with PD and 24 controls. Blood samples were withdrawn from subjects with PD and their matched controls. Biomarkers multiplex assay from Milliplex was used to assess the levels of IL-1B, IL-6, TNF-α, osteoprotegerin (OPG), osteopontin (OPN), and PTH (parathyroid hormone). Data was analyzed using the Statistical Package, GraphPad Prism. Results. We found that IL-1ß cytokine is significantly higher in patients with PD (p value = 0.0014). However, there are no statistically significant variances found among the two studied groups with regard to the IL-6 and TNF-α cytokines levels. We also found that levels of PTH are decreased in the PD subjects than the age-matched controls (p value= 0.003). Also, the bone matrix glycoproteins, including osteoprotegerin (OPG) and osteopontin (OPN), are significantly upregulated (p value= 0.04 for OPG and p value= 0.003 for OPN), as compared to the controls. Conclusions. Our findings are reliable with the possibility that inflammatory and bone markers can be used as biomarkers in PD prognosis. However, to clarify the natural role and consequence of these markers in PD pathology, further larger cohort studies are needed.

Pubmed ID: 31205938

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GraphPad Prism (tool)

RRID:SCR_002798

Statistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.

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